Table REF  contains the obtained results.
Our unaugmented model outperformed other published results, including BPL [1]} and Prototypical Networks [2]} and achieved perfect accuracy after applying Hungarian algorithm at test time, denoted by “full context” in the results. When training on the Minimal set, our model benefits from augmentation, and when full context is used, outperforms the previous state of the art, BPL. The model trained on points using data augmentation (three extra rotations per character) shows comparable accuracy to our image model, while the unaugmented results on points fall short of the corresponding image results. This is somewhat surprising, as one would assume that stroke trajectories contain the same amount of relevant information as the images. A likely reason for images performing better is the translation invariance induced by the convolutional network helping model to generalize better when compared to point-features prepared by simple MLPs.
